add device qc reportinG
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prisar
122
scripts/device_qc_report.py
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122
scripts/device_qc_report.py
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import pandas as pd
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import openpyxl
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import xlsxwriter
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import os
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import platform
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import numpy as np
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pd.options.mode.chained_assignment = None # default='warn'
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pd.options.display.float_format = '{:.4f}'.format
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precision_tolerance = 0.009
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accuracy_tolerance = 0.02
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def perform_calculations(curdir, path_delim, report_file):
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# print(curdir + path_delim + "hemocube_qc_09_08_2023_data.xlsx")
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df = pd.read_excel(curdir + path_delim + "hemocube_qc_09_08_2023_data.xlsx", sheet_name="qc_data")
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df_ref = pd.read_excel(curdir + path_delim + "hemocube_qc_09_08_2023_data.xlsx", sheet_name="reference_values")
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# df_ref['device - con'] = ""
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# for idx, row in df_ref.iterrows():
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# df_ref["device - con"][idx] = str(row["deviceId"]) + ", " + row["sol"] + ", " + row["wavelength"]
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print(df_ref)
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writer = pd.ExcelWriter(report_file, engine = 'xlsxwriter')
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# TODO rename led1Average to Abs 427 and led2Average to Abs555
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df = df.drop(["led1Sample", "deviceRatio", "led1Buffer", "testTime", "led2Sample", "led2Buffer"], axis=1)
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# duplicate columns
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df["device"] = df["deviceId"]
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df["device - con"] = ""
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for idx, row in df.iterrows():
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df['device - con'][idx] = row["deviceId"] + ", " + row["sol"] + ", " + row["wavelength"]
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# print(df)
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df_max = df.groupby(['deviceId', "sol"]).max()
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df_max['device - con'] = ""
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# print(df_max.iloc[1])
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df_min = df.groupby(['deviceId', "sol"]).min()
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df_min['device - con'] = ""
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for group, row in df_max.iterrows():
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df_max["device - con"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]
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df_max.to_excel(writer, sheet_name="max", index=False)
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for group, row in df_min.iterrows():
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df_min["device - con"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]
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# TODO: combine 427 mean and 555 mean using df.groupby(['device - con'])[["Abs 427", "Abs555"]].mean()
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df_427nm_mean = df.groupby(['device - con'])["Abs 427"].mean().to_frame()
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df_427nm_mean.rename(columns={'Abs 427': "Abs427_mean"}, inplace = True)
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df_555nm_mean = df.groupby(['device - con'])["Abs555"].mean().to_frame()
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df_555nm_mean.rename(columns={'Abs555': "Abs555_mean"}, inplace = True)
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# df_mean = df.copy()
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df_mean = df_427nm_mean.merge(df_555nm_mean, on="device - con")
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df_describe = df.groupby(['deviceId', "sol"]).describe()
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# print(df.groupby(['deviceId', "sol"]).describe())
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df_describe.to_excel(writer, sheet_name="describe")
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# for group, row in df_describe.iterrows():
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# df_mean["Abs555_mean"] = row["Abs 427"]["mean"]
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# print(df_mean)
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df_max_min = df_max.merge(df_min, on="device - con")
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df_max_min_mean = df_max_min.merge(df_mean, on="device - con")
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df_max_min_mean_ref = df_max_min_mean.merge(df_ref, on="device - con")
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df_min.to_excel(writer, sheet_name="min", index=False)
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df_max_min_mean_ref.to_excel(writer, sheet_name="df_max_min_mean_ref", index=False)
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df_result = df_max_min.copy()
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df_result["precision_427nm"] = df_max_min["Abs 427_x"] - df_max_min["Abs 427_y"]
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df_result["precision_555nm"] = df_max_min["Abs555_x"] - df_max_min["Abs555_y"]
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df_result["accuracy_427nm"] = df_max_min_mean_ref["ref_device_abs"] - df_max_min_mean_ref["Abs427_mean"]
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df_result["accuracy_555nm"] = df_max_min_mean_ref["ref_device_abs"] - df_max_min_mean_ref["Abs555_mean"]
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df_result = df_result.drop(["Abs 427_x", "Abs555_x", "wavelength_x", "Abs 427_y", "Abs555_y", "wavelength_y", "device_y"], axis=1)
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df_result = df_result.groupby('device_x')[['device_x', "device - con", "precision_427nm", "precision_555nm", "accuracy_427nm", "accuracy_555nm"]].apply(lambda x: x)
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# print(df_result)
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# for idx, row in df_result.iterrows():
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# print(idx, row)
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# df_result["precision_427nm"][1] = df_max.loc["Abs 427"][1] + df_min["Abs 427"][1]
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df_result.to_excel(writer, sheet_name="precision", index=False)
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workbook = writer.book
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worksheet = writer.sheets["precision"]
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format1 = workbook.add_format({"num_format": "#,##0.00000"})
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worksheet.set_column(180, 1, 35, format1)
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# Add a header format.
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header_format = workbook.add_format(
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{
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"bold": True,
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"text_wrap": True,
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"valign": "top",
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"fg_color": "#D7E4BC",
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"border": 1,
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}
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)
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# Write the column headers with the defined format.
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for col_num, value in enumerate(df_result.columns.values):
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worksheet.write(0, col_num, value, header_format)
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writer.close()
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if __name__ == "__main__":
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rootdir = os.getcwd()
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path_delim = ''
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if platform.system() == 'Darwin':
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path_delim = '/'
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else:
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path_delim = '\\'
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report_file = rootdir + path_delim + 'report.xlsx'
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print(report_file)
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perform_calculations(rootdir, path_delim, report_file)
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